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Estimation in the presence of many nuisance parameters: composite likelihood and plug-in likelihood

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  • Wu, Billy
  • Yao, Qiwei
  • Zhu, Shiwu

Abstract

We consider the incidental parameters problem in this paper, i.e. the estimation for a small number of parameters of interest in the presence of a large number of nuisance parameters. By assuming that the observations are taken from a multiple strictly stationary process, the two estimation methods, namely the maximum composite quasi-likelihood estimation (MCQLE) and the maximum plug-in quasi-likelihood estimation (MPQLE) are considered. For the MCQLE, we profile out nuisance parameters based on lower-dimensional marginal likelihoods, while the MPQLE is based on some initial estimators for nuisance parameters. The asymptotic normality for both the MCQLE and the MPQLE is established under the assumption that the number of nuisance parameters and the number of observations go to infinity together, and both the estimators for the parameters of interest enjoy the standard root-nn convergence rate. Simulation with a spatial–temporal model illustrates the finite sample properties of the two estimation methods.

Suggested Citation

  • Wu, Billy & Yao, Qiwei & Zhu, Shiwu, 2013. "Estimation in the presence of many nuisance parameters: composite likelihood and plug-in likelihood," LSE Research Online Documents on Economics 50043, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:50043
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    References listed on IDEAS

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    1. Tao, Minjing & Wang, Yazhen & Yao, Qiwei & Zou, Jian, 2011. "Large Volatility Matrix Inference via Combining Low-Frequency and High-Frequency Approaches," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 1025-1040.
    2. Kuk, Anthony Y. C. & Nott, David J., 2000. "A pairwise likelihood approach to analyzing correlated binary data," Statistics & Probability Letters, Elsevier, vol. 47(4), pages 329-335, May.
    3. Cavit Pakel & Neil Shephard & Kevin Sheppard & Robert F. Engle, 2021. "Fitting Vast Dimensional Time-Varying Covariance Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(3), pages 652-668, July.
    4. Tao, Minjing & Wang, Yahzen & Yao, Qiwei & Zou, Jian, 2011. "Large volatility matrix inference via combining low-frequency and high-frequency approaches," LSE Research Online Documents on Economics 39321, London School of Economics and Political Science, LSE Library.
    5. D. R. Cox, 2004. "A note on pseudolikelihood constructed from marginal densities," Biometrika, Biometrika Trust, vol. 91(3), pages 729-737, September.
    6. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
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    More about this item

    Keywords

    composite likelihood; incidental parameters problem; nuisance parameterlem; panel data; profile likelihood; quasi-likelihood; root-nn convergence;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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